Name
Abstract | ||
|---|---|---|
As computer graphics technology advances, it is becoming increasingly difficult to determine whether a given picture was taken by camera or via computer graphics. In this work, we propose a method to using simple CNN structures to identify photorealistic computer graphics (PRCG) using convolutional neural networks (CNN). This network trained to identify the source of image patches. We showed the network without pooling layer showed 98.2% accuracy, which is 2.1% higher than the result of using conventional object-recognition network. Testing random patches from image, the accuracy of identifying image reached 98.5%. Furthermore, it is possible to detect the photograph-PRCG synthesized regions from the image. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Digital Forensics, Image Source Identification, Convolutional Neural Networks, Photo-Realistic Computer Graphics |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Convolutional neural network,Computer science,Pooling,Robustness (computer science),Feature extraction,Artificial intelligence,Computer graphics | Conference | 1522-4880 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| In-Jae Yu | 1 | 10 | 2.86 |
| Do-Guk Kim | 2 | 12 | 2.36 |
| Jin-Seok Park | 3 | 7 | 2.59 |
| Jong-Uk Hou | 4 | 22 | 5.72 |
| Sunghee Choi | 5 | 19 | 3.03 |
| Heung-kyu Lee | 6 | 1016 | 87.53 |